super(channel_selection, self).__init__() self.indexes = nn.Parameter(torch.ones(num_channels))

时间: 2024-05-23 15:15:53 浏览: 180
这两行代码是一个类的构造函数,用于初始化类的属性。第一行代码调用了父类的构造函数并传递了 `channel_selection` 参数,以便父类可以正确地初始化。第二行代码创建了一个名为 `indexes` 的可训练参数(`nn.Parameter`),它是一个大小为 `num_channels` 的张量,每个元素都被初始化为 1。这个参数在模型训练时会被动态更新,用于选择输入数据中的哪些通道参与计算。
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def forward(self, data, org_edge_index): x = data.clone().detach() edge_index_sets = self.edge_index_sets device = data.device batch_num, node_num, all_feature = x.shape x = x.view(-1, all_feature).contiguous() gcn_outs = [] for i, edge_index in enumerate(edge_index_sets): edge_num = edge_index.shape[1] cache_edge_index = self.cache_edge_index_sets[i] if cache_edge_index is None or cache_edge_index.shape[1] != edge_num*batch_num: self.cache_edge_index_sets[i] = get_batch_edge_index(edge_index, batch_num, node_num).to(device) batch_edge_index = self.cache_edge_index_sets[i] all_embeddings = self.embedding(torch.arange(node_num).to(device)) weights_arr = all_embeddings.detach().clone() all_embeddings = all_embeddings.repeat(batch_num, 1) weights = weights_arr.view(node_num, -1) cos_ji_mat = torch.matmul(weights, weights.T) normed_mat = torch.matmul(weights.norm(dim=-1).view(-1,1), weights.norm(dim=-1).view(1,-1)) cos_ji_mat = cos_ji_mat / normed_mat dim = weights.shape[-1] topk_num = self.topk topk_indices_ji = torch.topk(cos_ji_mat, topk_num, dim=-1)[1] self.learned_graph = topk_indices_ji gated_i = torch.arange(0, node_num).T.unsqueeze(1).repeat(1, topk_num).flatten().to(device).unsqueeze(0) gated_j = topk_indices_ji.flatten().unsqueeze(0) gated_edge_index = torch.cat((gated_j, gated_i), dim=0) batch_gated_edge_index = get_batch_edge_index(gated_edge_index, batch_num, node_num).to(device) gcn_out = self.gnn_layers[i](x, batch_gated_edge_index, node_num=node_num*batch_num, embedding=all_embeddings) gcn_outs.append(gcn_out) x = torch.cat(gcn_outs, dim=1) x = x.view(batch_num, node_num, -1) indexes = torch.arange(0,node_num).to(device) out = torch.mul(x, self.embedding(indexes)) out = out.permute(0,2,1) out = F.relu(self.bn_outlayer_in(out)) out = out.permute(0,2,1) out = self.dp(out) out = self.out_layer(out) out = out.view(-1, node_num) return out

这是一个PyTorch模型的前向传播函数,它接受两个参数:data和org_edge_index。该函数使用GCN(图卷积神经网络)来处理输入数据,并返回一个输出张量。具体实现细节可以参考代码中的注释。

class AbstractGreedyAndPrune(): def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> MultiRoundSolution: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] visited_points |= set(opt_tour.targets_indexes) # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()) class CumulativeGreedyCoverage(AbstractGreedyAndPrune): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value] def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = (set(tour.targets_indexes) - visited_points) return round_count * len(new_points) 如何改写上述程序,使其能返回所有已经探索过的目标点visited_points的数量,请用代码表示

可以在 `solution()` 方法中添加一个变量来记录已经探索过的目标点数量,然后在每次更新 `visited_points` 后更新这个变量。下面是修改后的代码: ``` class AbstractGreedyAndPrune(): def __init__(self, aoi: AoI, uavs_tours: dict, max_rounds: int, debug: bool = True): self.aoi = aoi self.max_rounds = max_rounds self.debug = debug self.graph = aoi.graph self.nnodes = self.aoi.n_targets self.uavs = list(uavs_tours.keys()) self.nuavs = len(self.uavs) self.uavs_tours = {i: uavs_tours[self.uavs[i]] for i in range(self.nuavs)} self.__check_depots() self.reachable_points = self.__reachable_points() def __pruning(self, mr_solution: MultiRoundSolution) -> MultiRoundSolution: return utility.pruning_multiroundsolution(mr_solution) def solution(self) -> Tuple[MultiRoundSolution, int]: mrs_builder = MultiRoundSolutionBuilder(self.aoi) for uav in self.uavs: mrs_builder.add_drone(uav) residual_ntours_to_assign = {i : self.max_rounds for i in range(self.nuavs)} tour_to_assign = self.max_rounds * self.nuavs visited_points = set() explored_points = 0 while not self.greedy_stop_condition(visited_points, tour_to_assign): itd_uav, ind_tour = self.local_optimal_choice(visited_points, residual_ntours_to_assign) residual_ntours_to_assign[itd_uav] -= 1 tour_to_assign -= 1 opt_tour = self.uavs_tours[itd_uav][ind_tour] new_points = set(opt_tour.targets_indexes) - visited_points explored_points += len(new_points) visited_points |= new_points # update visited points mrs_builder.append_tour(self.uavs[itd_uav], opt_tour) return self.__pruning(mrs_builder.build()), explored_points class CumulativeGreedyCoverage(AbstractGreedyAndPrune): def evaluate_tour(self, tour : Tour, round_count : int, visited_points : set): new_points = set(tour.targets_indexes) - visited_points return round_count * len(new_points) def local_optimal_choice(self, visited_points, residual_ntours_to_assign): choice_dict = {} for ind_uav in range(self.nuavs): uav_residual_rounds = residual_ntours_to_assign[ind_uav] if uav_residual_rounds > 0: uav_tours = self.uavs_tours[ind_uav] for ind_tour in range(len(uav_tours)): tour = uav_tours[ind_tour] quality_tour = self.evaluate_tour(tour, uav_residual_rounds, visited_points) choice_dict[quality_tour] = (ind_uav, ind_tour) best_value = max(choice_dict, key=int) return choice_dict[best_value]
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class SVDRecommender: def __init__(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if self.which == 'LM': largest = True elif self.which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self.k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) #获得隐式定义的格拉米矩阵的低秩近似。 eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) #格拉米矩阵有实非负特征值。 eigvals = np.maximum(eigvals.real, 0) #使用来自pinvh的小特征值的复数检测。 t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) #获得一个指示哪些本征对不是简并微小的掩码, #并为阈值奇异值创建一个重新排序数组。 above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = self.k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not self.return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh def _augmented_orthonormal_cols(U, n): if U.shape[0] <= n: return U Q, R = np.linalg.qr(U) return Q[:, :n] def _augmented_orthonormal_rows(V, n): if V.shape[1] <= n: return V Q, R = np.linalg.qr(V.T) return Q[:, :n].T def _herm(x): return np.conjugate(x.T) 将上述代码修改为使用LM,迭代器使用arpack

from scipy.sparse.linalg import eigsh, LinearOperator from scipy.sparse import isspmatrix, is_pydata_spmatrix class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): largest = self.which == 'LM' if not largest and self.which != 'SM': raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if self.k <= 0 or self.k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % self.k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) eigvals, eigvec = eigsh(XH_X, k=self.k, tol=self.tol ** 2, maxiter=self.maxiter, ncv=self.ncv, which=self.which, v0=self.v0) eigvals = np.maximum(eigvals.real, 0) t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = self.k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not self.return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if self.return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if self.return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh def _augmented_orthonormal_cols(U, n): if U.shape[0] <= n: return U Q, R = np.linalg.qr(U) return Q[:, :n] def _augmented_orthonormal_rows(V, n): if V.shape[1] <= n: return V Q, R = np.linalg.qr(V.T) return Q[:, :n].T def _herm(x): return np.conjugate(x.T)这段代码中使用的scipy包太旧了,导致会出现报错信息为:cannot import name 'is_pydata_spmatrix' from 'scipy.sparse' (D:\Anaconda\lib\site-packages\scipy\sparse_init.py),将这段代码修改为使用最新版的scipy包

class SVDRecommender: def init(self, k=50, ncv=None, tol=0, which='LM', v0=None, maxiter=None, return_singular_vectors=True, solver='arpack'): self.k = k self.ncv = ncv self.tol = tol self.which = which self.v0 = v0 self.maxiter = maxiter self.return_singular_vectors = return_singular_vectors self.solver = solver def svds(self, A): if which == 'LM': largest = True elif which == 'SM': largest = False else: raise ValueError("which must be either 'LM' or 'SM'.") if not (isinstance(A, LinearOperator) or isspmatrix(A) or is_pydata_spmatrix(A)): A = np.asarray(A) n, m = A.shape if k <= 0 or k >= min(n, m): raise ValueError("k must be between 1 and min(A.shape), k=%d" % k) if isinstance(A, LinearOperator): if n > m: X_dot = A.matvec X_matmat = A.matmat XH_dot = A.rmatvec XH_mat = A.rmatmat else: X_dot = A.rmatvec X_matmat = A.rmatmat XH_dot = A.matvec XH_mat = A.matmat dtype = getattr(A, 'dtype', None) if dtype is None: dtype = A.dot(np.zeros([m, 1])).dtype else: if n > m: X_dot = X_matmat = A.dot XH_dot = XH_mat = _herm(A).dot else: XH_dot = XH_mat = A.dot X_dot = X_matmat = _herm(A).dot def matvec_XH_X(x): return XH_dot(X_dot(x)) def matmat_XH_X(x): return XH_mat(X_matmat(x)) XH_X = LinearOperator(matvec=matvec_XH_X, dtype=A.dtype, matmat=matmat_XH_X, shape=(min(A.shape), min(A.shape))) # Get a low rank approximation of the implicitly defined gramian matrix. eigvals, eigvec = eigsh(XH_X, k=k, tol=tol ** 2, maxiter=maxiter, ncv=ncv, which=which, v0=v0) # Gramian matrix has real non-negative eigenvalues. eigvals = np.maximum(eigvals.real, 0) # Use complex detection of small eigenvalues from pinvh. t = eigvec.dtype.char.lower() factor = {'f': 1E3, 'd': 1E6} cond = factor[t] * np.finfo(t).eps cutoff = cond * np.max(eigvals) # Get a mask indicating which eigenpairs are not degenerate tiny, # and create a reordering array for thresholded singular values. above_cutoff = (eigvals > cutoff) nlarge = above_cutoff.sum() nsmall = k - nlarge slarge = np.sqrt(eigvals[above_cutoff]) s = np.zeros_like(eigvals) s[:nlarge] = slarge if not return_singular_vectors: return np.sort(s) if n > m: vlarge = eigvec[:, above_cutoff] ularge = X_matmat(vlarge) / slarge if return_singular_vectors != 'vh' else None vhlarge = _herm(vlarge) else: ularge = eigvec[:, above_cutoff] vhlarge = _herm(X_matmat(ularge) / slarge) if return_singular_vectors != 'u' else None u = _augmented_orthonormal_cols(ularge, nsmall) if ularge is not None else None vh = _augmented_orthonormal_rows(vhlarge, nsmall) if vhlarge is not None else None indexes_sorted = np.argsort(s) s = s[indexes_sorted] if u is not None: u = u[:, indexes_sorted] if vh is not None: vh = vh[indexes_sorted] return u, s, vh将这段代码放入一个.py文件中,用Spyder查看,有报错,可能是缩进有问题,无法被调用,根据这个问题,给出解决办法,给出改正后的完整代码

bs_data = w_data[w_data['波束场景No'] == cross_data['波束场景No'][0]] 报错:Traceback (most recent call last): File "C:\Users\z84259074\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\indexes\base.py", line 3652, in get_loc return self._engine.get_loc(casted_key) File "pandas\_libs\index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas\_libs\hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: '波束场景No' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "D:\Users\z84259074\PycharmProjects\参数自优化\self_optimizing.py", line 127, in <module> data = optimizing() File "D:\Users\z84259074\PycharmProjects\参数自优化\self_optimizing.py", line 18, in __init__ self.optimizing_main() File "D:\Users\z84259074\PycharmProjects\参数自优化\self_optimizing.py", line 120, in optimizing_main self.child2=self.mutation_cdata(fitness_data,self.cross_data) File "D:\Users\z84259074\PycharmProjects\参数自优化\self_optimizing.py", line 86, in mutation_cdata bs_data = w_data[w_data['波束场景No'] == cross_data['波束场景No'][0]] File "C:\Users\z84259074\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\frame.py", line 3761, in __getitem__ indexer = self.columns.get_loc(key) File "C:\Users\z84259074\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\indexes\base.py", line 3654, in get_loc raise KeyError(key) from err KeyError: '波束场景No'

(random.randint(0, bs_data[12]-1))*3+bs_data[8] 报错:Traceback (most recent call last): File "C:\Users\z84259074\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\indexes\base.py", line 3652, in get_loc return self._engine.get_loc(casted_key) File "pandas\_libs\index.pyx", line 147, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\index.pyx", line 176, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\hashtable_class_helper.pxi", line 7080, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas\_libs\hashtable_class_helper.pxi", line 7088, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 12 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "d:\Users\z84259074\PycharmProjects\参数自优化\self_optimizing.py", line 128, in <module> data = optimizing() File "d:\Users\z84259074\PycharmProjects\参数自优化\self_optimizing.py", line 15, in __init__ self.optimizing_main() File "d:\Users\z84259074\PycharmProjects\参数自优化\self_optimizing.py", line 124, in optimizing_main self.child2=self.mutation_cdata(fitness_data,self.cross_data) File "d:\Users\z84259074\PycharmProjects\参数自优化\self_optimizing.py", line 92, in mutation_cdata print('cross_data[波束场景No]',bs_data[12]) File "C:\Users\z84259074\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\frame.py", line 3761, in __getitem__ indexer = self.columns.get_loc(key) File "C:\Users\z84259074\AppData\Local\Programs\Python\Python310\lib\site-packages\pandas\core\indexes\base.py", line 3654, in get_loc raise KeyError(key) from err KeyError: 12

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